The following relates generally to object detection, and more specifically to symmetry exploitation for object detection.
Object detection may refer to a field of computer vision for finding objects in an image or video sequence and distinguishing them from other aspects of the image or video sequence. At a high level of abstraction, object detection may be implemented by extracting features from an image and comparing those features with threshold values. For example, the extracted features may be operated on by one or more classifiers of an object model. The classifiers may operate on the features by comparing the values of the features to threshold values as defined by the object model. Each classifier may return an output value based on the outcome of the comparisons, and the values from multiple classifiers may be aggregated to determine whether an object is in fact within the image.
For a single category of object there may be many object models so that the object can be recognized in different orientations. For example, for vehicle detection there may be an object model for every orientation of a vehicle with respect to the camera (e.g., head-on versus broadside, etc.). In order for an object model to work properly, it may be trained and stored prior to use. But training and storing object models for each orientation of an object category may require a significant amount of time and memory.